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IEICE Transactions on Information and Systems 2008 E91-D(4):969-975; doi:10.1093/ietisy/e91-d.4.969
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Copyright © 2008 The Institute of Electronics, Information and Communication Engineers

Special Section on Knowledge-Based Software Engineering -- Papers -- Knowledge Engineering

Sentence Topics Based Knowledge Acquisition for Question Answering

Hyo-Jung OH1 and Bo-Hyun YUN2

1 The author is with ETRI, 161 Gajeong-dong, Yuseong-gu, Daejeon, 350–700, Korea., 2 The author is with Mokwon University, Mokwon Gil 21, Seo-gu, Daejeon, 302–318, Korea. E-mail: ybh{at}mokwon.ac.kr


  Abstract

This paper presents a knowledge acquisition method using sentence topics for question answering. We define templates for information extraction by the Korean concept network semi-automatically. Moreover, we propose the two-phase information extraction model by the hybrid machine learning such as maximum entropy and conditional random fields. In our experiments, we examined the role of sentence topics in the template-filling task for information extraction. Our experimental result shows the improvement of 18% in F-score and 434% in training speed over the plain CRF-based method for the extraction task. In addition, our result shows the improvement of 8% in F-score for the subsequent QA task.

Key Words: knowledge acquisition, machine learning, question answering

Manuscript received July 2, 2007. Manuscript revised September 28, 2007.


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